Function-on-Function Linear Regression by Signal Compression

We consider functional linear regression models with a functional response and multiple functional predictors, with the goal of finding the best finite-dimensional approximation to the signal part of the response function. Defining the integrated squared correlation coefficient between a random vari...

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Vydané v:	Journal of the American Statistical Association Ročník 112; číslo 518; s. 690 - 705
Hlavní autori:	Luo, Ruiyan, Qi, Xin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Alexandria Taylor & Francis 01.06.2017 Taylor & Francis Group,LLC Taylor & Francis Ltd
Predmet:	Americans Compression Convergence Correlation coefficients Eigenvalues Finite-dimensional approximation to coefficient kernel function Function Function-on-function linear regression functional response models Integrated squared correlation coefficient Integrated squared covariance least squares linear models objectives Penalized generalized functional eigenvalue problem Performance prediction Projections Random variables Regression analysis Regression models Response functions Scalarity (Semantics) Signal compression Simulation Statistics Theory and Methods
ISSN:	0162-1459, 1537-274X, 1537-274X
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Popis
Shrnutí:	We consider functional linear regression models with a functional response and multiple functional predictors, with the goal of finding the best finite-dimensional approximation to the signal part of the response function. Defining the integrated squared correlation coefficient between a random variable and a random function, we propose to solve a penalized generalized functional eigenvalue problem, whose solutions satisfy that projections on the original predictors generate new scalar uncorrelated variables and these variables have the largest integrated squared correlation coefficient with the signal function. With these new variables, we transform the original function-on-function regression model to a function-on-scalar regression model whose predictors are uncorrelated, and estimate the model by penalized least-square method. This method is also extended to models with both multiple functional and scalar predictors. We provide the asymptotic consistency and the corresponding convergence rates for our estimates. Simulation studies in various settings and for both one and multiple functional predictors demonstrate that our approach has good predictive performance and is very computational efficient. Supplementary materials for this article are available online.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	0162-1459 1537-274X 1537-274X
DOI:	10.1080/01621459.2016.1164053